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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Voicu, Razvan Cristian Freeman, Charles F. Garretson, Christopher E. Sun, Zoe M. Bonus, Ryan H. Slutzky, Zachary A. Mukesh, Niranjan Tanveer, Muhammad Hassan Tello, Yannique Chang, Yusun Kennesaw State University Department of Robotics & Mechatronics Engineering MariettaGA United States

ISBN: (纸本)9798331504847

Wheelchair users with limited or nonexistent lower-limb mobility often struggle with significant muscle atrophy, circulatory complications, and other health issues. Although exoskeletons for gait training have shown promise, many are optimized for upright ambulation and overlook individuals remaining fully seated due to clinical or practical constraints. This article presents a seat-focused exoskeleton synergy integrated with a wheelchair to facilitate multi-DOF lower-limb mobilization without requiring transfers or standing maneuvers. The system addresses a critical gap by providing repeated, lower-intensity knee and ankle exercises throughout the day-either passively, actively with assistance, or in a resistive training mode-thus promoting muscle engagement and circulation from a seated position. The exoskeleton leverages the wheelchair's power source and employs modular, adjustable rails to accommodate diverse leg lengths. Emphasis is placed on biomechanical alignment, safe torque control, and user comfort, supported by a model-based controller accounting for inertia, Coriolis effects, and gravity in real time. Bench tests and preliminary trials confirm stable torque output, accurate tracking of desired motion profiles, and minimal burden when attaching or detaching the device. The article introduces modeling for a two-DOF prototype (knee flexion/extension and ankle dorsiflexion/plantarflexion), expandable to include additional joints or degrees of freedom, enabling broader rehabilitation targets such as hip abduction/adduction. By embedding rehabilitative capabilities directly into the wheelchair environment, the proposed approach seeks to close the gap between infrequent clinic appointments and continuous therapy aimed at mitigating muscle ailments. A seat-focused exoskeleton designed for every-day use can facilitate accessible, on-demand rehabilitation and substantially improve long-term health outcomes for mobility impaired users. © 2025 IEEE.

关键词： Physical therapy

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Humanoid robotics Powered by Vision-Based LLM Zero Shot Interactions

Humanoid Robotics Powered by Vision-Based LLM Zero Shot Inte...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Smith, Gabriel David Debrah-Pinamang, Kwasi Voicu, Razvan Cristian Kennesaw State University Department of Robotics & Mechatronics Engineering MariettaGA United States

ISBN: (纸本)9798331504847

Advancements in artificial intelligence (AI) have transformed robotics by enabling systems to autonomously execute complex tasks with minimal human involvement. Traditional methods, however, often depend on costly hardware, continuous monitoring, and intricate software integration, which constrain scalability and widespread implementation. This research presents an innovative approach that combines vision-based large language models (LLMs) with zero-shot prompting to autonomously program robotic systems, including a humanoid robot equipped with dual manipulators. The proposed system harnesses contextual image data to efficiently generate task-specific code, eliminating the need for iterative corrections. Training is conducted through OpenAI's Assistant feature, utilizing documents predominantly comprising images, while continuous operation is facilitated by a self-looping mechanism. Experimental results highlight the system's capability to perform manipulator tasks with notable accuracy, paving the way for scalable, adaptive, and dynamic automation. This study addresses both practical and theoretical challenges in automation, providing a cost-effective framework for next-generation robotic systems. © 2025 IEEE.

关键词： Anthropomorphic robots

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AREPO: Uncertainty-Aware Robot Ensemble Learning under Extreme Partial Observability

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IEEE robotics and Automation Letters 2025年第6期10卷 5737-5744页

作者： Du, Yurui Hanut, Louis Bruyninckx, Herman Detry, Renaud Research Unit Processing Speech and Images KU Leuven Department of Electrical Engineering LeuvenB-3000 Belgium Research Unit Robotics Automation and Mechatronics KU Leuven Department of Mechanical Engineering LeuvenB-3000 Belgium

Real-world applications of vision-based robot learning face two major challenges: extreme partial observability and effective simulation-to-reality (sim-to-real) transfer. This letter introduces a robust robot learning framework that enhances uncertainty awareness to address these challenges. We reinterpret variational-autoencoder-based visual reinforcement learning (RL) from an uncertainty-quantification perspective, enabling resilience to high sensory noise and severe visual occlusions-common in industrial robotic tasks. To further improve sim-to-real transfer, we propose an uncertainty-aware ensemble RL algorithm. We validate our methods on a laboratory task designed as a proxy for real-world industrial applications characterized by harsh environments with low visibility and physical occlusions. Both simulation and real-world results demonstrate significant improvements in task accuracy and efficiency over various baselines, highlighting the benefits of uncertainty-aware robot learning for complex operational contexts. © 2016 IEEE.

关键词： Adversarial machine learning

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Self-supervised Deformable Registration of Digital Tomosynthesis and 3D CT Images for Surgical Navigation

Self-supervised Deformable Registration of Digital Tomosynth...

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Medical Imaging 2025: Image Processing

作者： Park, Muyul So, Jeongtae Jung, Young-Jun Ito, Mikiko Lee, Byung Kee Lee, Gyeongmin Park, Sang Hyun LG Electronics Seoul Korea Republic of Department of Robotics and Mechatronics Engineering DGIST Daegu Korea Republic of

ISBN: (纸本)9781510685901

To enhance the precision and efficiency of surgical procedures, there is an increasing demand for integrating medical imaging systems into the surgical environment. For example, a mobile C-Arm image has been widely applied to intraoperative imaging system. However, the projective nature of C-Arm image limits its effectiveness for accurate surgical guidance. Recently, a mobile digital tomosynthesis (DTS) system has emerged as an attractive alternative, providing quasi-3D images with a radiation dose comparable to that of conventional 2D radiography and ease of use in the intraoperative scene. To enhance the utility of mobile DTS, it is required to register DTS with preoperative CT. However, the previously proposed registration methods have shown insufficient results due to the limited depth resolution and the streaking artifact inherent to DTS. Therefore, we propose a novel self-supervised learning-based 3D deformable registration between CT and mobile DTS by introducing a dataset generation technique to train the deep learning network with an additional constraint. The proposed registration algorithm is composed of a sequential process involving an affine registration based on iterative optimization and a deformable registration based on self-supervised learning. To evaluate the registration accuracy, we compared the distances between matching points. We observed that the 3D distance decreased from 14.07±1.79 voxels to 3.73±0.53 voxels after affine registration and to 2.55±0.50 voxels after the self-supervised deformable registration. © 2025 SPIE.

关键词： Image registration

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Modelling and experimental investigation of micro-dimpled structures milling with spiral trajectory tool reciprocating motion

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Chinese Journal of Aeronautics 2025年第2期38卷 577-596页

作者： Guangzhou WANG Linjie ZHAO Qi LIU Xiguang LI Yazhou SUN Mingjun CHEN School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China Centre for Precision Manufacturing DMEMUniversity of StrathclydeGlasgow G11XJUK State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China

To mill fine and well-defined micro-dimpled structures,a machining manner of spiral trajectory tool reciprocating motion,where the tool repeats the process of‘feed milling–retract–cutting feed–feed milling again’along the spiral trajectory,was *** the kinematics analysis,it is found that the machining quality of micro-dimpled structures is highly dependent on the machining trajectory using spiral trajectory tool reciprocating *** reveal this causation,simulation modelling and experimental studies were carried out.A simulation model was developed to quantitatively and qualitatively investigate the influence of the trajectory discretization strategies(constant-angle and constant-arc length)and parameters(discrete angle,discrete arc length,and pitch)on surface texture and residual height of micro-dimpled ***,micro-dimpled structures were milled under different trajectory discretization strategies and parameters with spiral trajectory tool reciprocating motion.A comprehensive comparison between the milled results and simulation analysis was made based on geometry accuracy,surface morphology and surface roughness of milled ***,the errors and factors affecting the above three aspects were *** results demonstrate both the feasibility of the established simulation model and the machining capability of this machining way in milling high-quality micro-dimpled *** trajectory tool reciprocating motion provides a new machining way for milling micro-dimpled structures and micro-dimpled functional *** an appropriate machining trajectory can be generated based on the optimized trajectory parameters,thus contributing to the improvement of machining quality and efficiency.

关键词： Tool reciprocating motion Spiral trajectory Micro-dimpled structure Micromachining Simulation modelling with diamond tool Surface texture

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A Study on Improving the Performance of Dynamic Model Based Redundant Navigation System for Sensor Fault Scenarios

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Journal of Institute of Control, robotics and Systems 2025年第4期31卷 351-359页

作者： Ko, Bo Sung Choi, Hyeong Jun Jeon, Myung Jun Cho, Hyeon Jin Song, Jin Woo Department of Artificial Intelligence and Robotics and Convergence Engineering for Intelligent Drone AIRI Sejong University Korea Republic of Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone AIRI Sejong University Korea Republic of Agency for Defense Development Maritime Technology Research Institute Korea Republic of

This study proposes a redundant navigation system for UUV (Unmanned Underwater Vehicle) utilizing a 6 DOF (Degree of Freedom) VDM (Vehicle Dynamic Model). The system is designed to enable safe UUV asset return when all navigation sensors become inoperable. Since it is impossible to perfectly determine VDM coefficients through water tank experiments, a method to improve VDM output accuracy in real time during UUV mission is necessary. Here in this study, we introduce real-time adaptive 6 DOF VDM output error compensation algorithm to enhance the performance of redundant navigation systems. This research aims to establish the foundation for backup navigation solutions in situations when critical UUV sensor failures occur. © ICROS 2025.

关键词： Water tanks

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Multiparty Random Phase Wrapping Secret-Sharing Systems for Visual Data Security

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3586-3600页

作者： Kim, Youhyun Jeong, Ongee Moon, Inkyu Javidi, Bahram Daegu Gyeongbuk Institute of Science and Technology Department of Robotics and Mechatronics Engineering Daegu42988 Korea Republic of AhnLab Inc. Gyeonggi Seongnam13493 Korea Republic of University of Connecticut Department of Electrical and Computer Engineering StorrsCT06269 United States

A secret sharing scheme is an important cryptographic procedure that enables the secure distribution of secret information, such as private images, in an untrusted network. However, in all secret sharing schemes, the sizes of the shares increase in proportion to the size of the secret information, because they involve computationally expensive polynomials of degree n-1 (n is total number of shares) and increasingly large modulus of modulo operations for security. Moreover, with large share sizes, it is not easy in practice to encrypt them quickly using block cipher algorithms. Therefore, ensuring the security of secret sharing for large-scale visual data with a reasonable share length and efficiently encrypting n shares properly represents formidable challenges. To overcome these challenges in secret sharing schemes, we propose new multiparty random phase wrapping secret sharing systems for visual datasets. Computational optical imaging enables the acquisition of wrapped phase information, ranging from–π to π, to be expressed in the form of a complex sinusoidal waveform. The proposed scheme allows for large-scale secret data—such as confidential digital images and visual data, including optical images—to be securely and efficiently shared and distributed to multiple parties or agencies utilizing a digital representation of a complex sinusoidal waveform of the secret information. The proposed scheme can be useful in cryptographic key escrow systems and in use with large-scale secret data. © 2013 IEEE.

关键词： Data Sharing

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Generalized Dynamics of Non-Grounded Systems with Series Elastic Actuators and Effect of Actuator Base Fixation on Force Control Robustness

Generalized Dynamics of Non-Grounded Systems with Series Ela...

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International Conference on mechatronics (ICM)

作者： Wonhyeok Choi Sehoon Oh Department of Robotics and Mechatronics Engineering DGIST Daegu Korea

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

This paper proposes a generalized dynamics model for a Non-Grounded System (NGS) mounted with a Reaction Force Sensing Series Elastic Actuator (RFSEA), focusing on scenarios where the actuator's base is unfixed and interacts with the ground. In RFSEA-based force control, the actuator base, which provides the necessary reaction force as dictated by Newton's third law, plays a critical role. This study examines how the interaction between the actuator base and the ground it is attached to influences the response of the series elasticity that generates force in the RFSEA. The investigation involves two steps. First, a generalized dynamics model that considers the dynamics between the actuator base and the ground is derived. Second, the Small Gain Theorem (SGT) is applied to evaluate the robustness of force control under load-side model uncertainties when a Force-based Disturbance Observer (FDOB), originally designed for GS, is adapted to NGS. The analysis reveals that force control utilizing FDOB can become unstable when the actuator base is unfixed, and simulations validate these findings.

关键词： Actuators Analytical models Adaptation models Uncertainty Dynamics Force Elasticity Robustness Force control Load modeling

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A deep learning-based model for automated STN localization using local field potentials in Parkinson’s disease

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Soft Computing 2025年第4期29卷 2343-2362页

作者： Hosny, Mohamed Naeem, Mohamed A. Zhu, Minwei Gao, Wenpeng Elshenhab, Ahmed M. Fu, Yili Department of Electrical Engineering Benha Faculty of Engineering Benha University Benha Egypt State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150080 China Department of Mechatronics and Robotics Engineering Faculty of Engineering South Valley University Qena Egypt First Affiliated Hospital Harbin Medical University Harbin150001 China School of Life Science and Technology Harbin Institute of Technology Harbin150080 China Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt

Accurate identification of the subthalamic nucleus (STN) borders is time-consuming, relying heavily on the neurosurgeon expertise in manually interpreting the electrophysiological signals. Local field potentials (LFPs) have garnered imperative attention due to their strong correlation with the STN. However, existing detection models often face challenges with high computational complexity, hyperparameter optimization and lack of explainability, making them unreliable for clinicians. Therefore, this study introduces an explanatory framework using convolutional neural networks (CNN) for detecting the STN region from LFPs. Continuous wavelet transform is employed to convert LFPs signals into scalogram images, which are then processed by sixteen CNN models. We evaluated our framework by examining the impact of various limiting factors on the classification performance, including model size, learning rate (LR), optimizers and data scaling. Deep features are extracted from the top-performing CNN architectures to capture rich representations of the scalograms. These features are then fused and classified using k-nearest neighbour algorithm. Gradient-weighted class activation mapping is used to explain the decisions made by the proposed model. Our approach achieved an accuracy of 99.61%, outperforming individual CNN models for STN localization. The experimental results revealed that CNN models, embedded with additional hyperparameters and layers, generally outperformed smaller models. Besides, low LR significantly enhanced the performance compared to high LR. Moreover, features extracted from untuned networks produced lower performance than tuned networks. The proposed system could revolutionize deep brain stimulation surgery by increasing efficiency and reducing reliance on clinician expertise for STN detection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Wavelet transforms

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TAGA: A Tangent-Based Reactive Approach for Socially Compliant Robot Navigation Around Human Groups

arXiv

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arXiv 2025年

作者： Roy, Utsha Kumar Rahman, Sejuti The Department of Robotics and Mechatronics Engineering University of Dhaka Dhaka Bangladesh

Robot navigation in densely populated environments presents significant challenges, particularly regarding the interplay between individual and group dynamics. Current navigation models predominantly address interactions with individual pedestrians while failing to account for human groups that naturally form in real-world settings. Conversely, the limited models implementing group-aware navigation typically prioritize group dynamics at the expense of individual interactions, both of which are essential for socially appropriate navigation. This research extends an existing simulation framework to incorporate both individual pedestrians and human groups. We present Tangent Action for Group Avoidance (TAGA), a modular reactive mechanism that can be integrated with existing navigation frameworks to enhance their group-awareness capabilities. TAGA dynamically modifies robot trajectories using tangent action-based avoidance strategies while preserving the underlying model’s capacity to navigate around individuals. Additionally, we introduce Group Collision Rate (GCR), a novel metric to quantitatively assess how effectively robots maintain group integrity during navigation. Through comprehensive simulation-based benchmarking, we demonstrate that integrating TAGA with state-of-the-art navigation models (ORCA, Social Force, DS-RNN, and AG-RL) reduces group intrusions by 45.7–78.6% while maintaining comparable success rates and navigation efficiency. Future work will focus on real-world implementation and validation of this approach. Additional details and resources are available at https:// ***/***/taga/home. Copyright © 2025, The Authors. All rights reserved.

关键词： Population dynamics

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